RESEARCH & FIELD EXPERIMENTS CERTIFICATIONS (5 CERTIFICATIONS)
All research and field experiments certifications are grounded in IGL Masterclass training and real IITA-CGIAR field experience, and aligned to J-PAL methodology, World Bank DIME and LSMS standards, CGIAR research standards, Cochrane and Campbell Collaboration methods, and CONSORT/PRISMA reporting guidelines.
CFEP — Certified Field Experiment Practitioner (CFEP)
“Rigorous evidence. Transformative decisions. Global impact.” A globally rigorous field experiments certification grounded in completion of all 8 sessions of the IGL Designing Field Experiments Masterclass Series. Covers the full lifecycle of randomised controlled trials — from design and pre-registration through data collection, analysis, and policy dissemination. |
| Programme Details | Information |
|---|---|
|
Level |
Postgraduate & Professional |
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Audience |
Researchers, NGO evaluators, policy analysts, development economists, government research units, and postgraduate students |
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Standards |
IGL (Innovation Growth Lab) Masterclass Standards · J-PAL Research Methodology · World Bank DIME Standards · CONSORT RCT Reporting Guidelines · AEA Pre-registration Standards |
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Duration |
11 months (1 month per module) |
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Format |
Self-paced · Live instructor-led · Cohort-based · Blended |
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Assessment |
Proctored examination (minimum 75%) + complete pre-analysis plan submission + panel presentation |
|
Certificate |
CFEP Certificate — Ukeh-Adah Alliance Services Ltd |
Course modules
What field experiments are and why they matter for evidence-based policy · History: from agricultural RCTs to development economics and business experiments · Causal inference: the fundamental problem and how randomisation solves it · Types: RCTs, natural experiments, quasi-experiments, lab-in-field, and A/B tests · Ethics: informed consent, IRB approval, and do-no-harm principles |
Formulating testable research questions and causal hypotheses · Defining treatment arms, control groups, and comparison conditions · Randomisation strategies: simple, stratified, cluster, and matched-pairs · Unit of randomisation: individual, household, firm, community, or school · Pre-analysis plans: components, purpose, and registering on AEA Registry or OSF |
Type I and Type II errors: alpha, beta, and the consequences of each · Power calculations: the four determinants of adequate sample size · Minimum Detectable Effect (MDE): defining meaningful effect sizes · Intracluster Correlation Coefficient (ICC) in cluster randomised trials · Software: G*Power, Stata, R (pwr package), and online calculators |
Designing survey instruments: validated scales and custom measurement tools · Baseline, midline, and endline data collection protocols · Avoiding measurement bias: social desirability, recall, and interviewer effects · Digital data collection: SurveyCTO, ODK, and KoboToolbox · Data quality assurance: back-checks, logic checks, field audits, outlier detection |
Attrition: differential vs random, Lee bounds, and Horowitz-Manski bounds · Spillover effects and contamination: detection and the SUTVA assumption · Non-compliance: Intent-to-Treat (ITT) vs Local Average Treatment Effect (LATE/IV) · Multiple hypothesis testing: FWER, FDR, and pre-specified outcomes · Hawthorne effect and experimenter demand: minimising and documenting |
Designing experiments inside companies: HR, operations, and management · Getting management and ethics board buy-in for internal experiments · Testing training programmes, incentives, nudges, and management practices · Protecting commercial confidentiality and proprietary data ethically · IGL Masterclass case studies: real firm-level RCTs and lessons learned |
Measuring entrepreneur outcomes: revenue, employment, survival, and capability · Testing business support, mentorship, and accelerator interventions · Innovation experiments: product, process, market, and organisational change · Working with incubators, innovation hubs, and government enterprise programmes · IGL evidence review: global evidence on what works for entrepreneurs |
AI-assisted survey design: adaptive questionnaires and intelligent skip logic · Machine learning for heterogeneous treatment effects (HTE): Causal Forests and GRF · NLP for qualitative data: automated coding, sentiment analysis, topic modelling · AI-enhanced data quality: automated back-checks and anomaly detection · Ethical considerations: transparency, algorithmic bias, and AI in human subjects research |
Translating experimental results into policy-relevant language · Writing policy briefs: structure, evidence hierarchy, and communicating uncertainty · Effect sizes, confidence intervals, and what ‘significant’ means for policy · Building sustainable research partnerships with governments and firms · Navigating political and institutional constraints in applied research |
Academic publishing: journal selection, submission, peer review, and revision · Pre-registration and open science: reproducibility and transparency · Writing the results section: tables, figures, and interpreting coefficients · Conference presentations: structuring a 15-minute research talk · Policy dissemination: J-PAL bulletins, VoxDev, IGC blogs, and media |
Choose a real-world research question with policy or business relevance · Write a complete pre-analysis plan (PAP) to AEA Registry standard · Design the full experiment: randomisation, power calculation, instruments, timeline · Live canvas feedback: present design to panel for structured critique · Final submission: revised design incorporating all panel recommendations |
Design, power, and implement RCTs to the highest global standards · Collect rigorous data and apply ITT and LATE estimation · Navigate attrition, spillovers, and the five main threats to validity · Communicate findings to policymakers, firms, and academic audiences · Achieve a credential grounded in IGL, J-PAL, World Bank DIME, and AEA standards |
Complete all 11 modules, submit a complete pre-analysis plan (PAP) registered on the AEA Registry or OSF, and present the experiment design to a review panel (minimum 75%). |
Impact Evaluator, Development Economist, Research Manager (NGO/Government), J-PAL/IGL Research Affiliate, Policy Analyst, Academic Researcher. Average starting salary: $55,000–$95,000 USD. |
CES — Certified Evidence Synthesist (CES) — Systematic Reviews & Meta-Analysis
★ Why this certification was added
The world’s most influential policymakers and funders — World Bank, USAID, Gates Foundation, EU, Cochrane, Campbell, and 3ie — require systematic reviews and meta-analysis, not just single experiments. A single RCT answers ‘did this work here?’ — a systematic review answers ‘what does ALL the evidence tell us?’ Without this, Ukeh-Adah Alliance Services cannot serve the highest-tier research clients or compete at the top of the global evidence ecosystem.
“One review. Decades of evidence. Decisions that last.” A rigorous, internationally benchmarked certification in systematic review methodology and meta-analysis — aligned to Cochrane, Campbell, 3ie, and PRISMA 2020 standards. Graduates synthesise bodies of evidence to directly inform national and international policy decisions. |
| Programme Details | Information |
|---|---|
|
Level |
Postgraduate & Professional |
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Audience |
Researchers, policy analysts, public health professionals, development economists, NGO evaluators, academic faculty, and international organisation staff |
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Standards |
Cochrane Collaboration Methods (version 6.4) · Campbell Collaboration Guidelines · PRISMA 2020 Reporting Checklist · 3ie Systematic Review Standards · What Works Clearinghouse (WWC) · GRADE Evidence Framework · PROSPERO Registration Standards |
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Duration |
6 months (1 month per module) |
|
Format |
Self-paced · Live instructor-led · Cohort-based · Blended |
|
Assessment |
Proctored examination (minimum 75%) + registered systematic review protocol (PROSPERO/OSF) + meta-analysis exercise |
|
Certificate |
CES Certificate — Ukeh-Adah Alliance Services Ltd |
Course modules
What evidence synthesis is and why it matters for evidence-informed policy · Evidence hierarchies: why systematic reviews outrank single studies and expert opinion · Types of reviews: systematic, scoping, rapid, narrative, realist, umbrella, and living · When to conduct a review: feasibility assessment and avoiding duplication · Cochrane and Campbell Collaborations: structure, resources, and standards · 3ie Development Evidence Portal (DEP) and the global evidence ecosystem · PROSPERO and OSF: international registers — why pre-registration is mandatory · Publication bias and its consequences for policy recommendations |
PICO framework: Population, Intervention, Comparison, Outcome · PICOS and PECO: adding Study design and Exposure · Eligibility criteria: inclusion and exclusion — explicit, exhaustive, and reproducible · Writing a systematic review protocol to Cochrane standard · Registering on PROSPERO: step-by-step guide and common errors to avoid · Managing scope: too broad vs too narrow — finding the right balance · Review team roles: responsibilities and conflict of interest disclosure · Realistic timeline planning: milestones for a 6–18 month review · Hands-on lab: write and register a complete protocol on OSF |
Why sensitivity matters more than precision in systematic searching · Bibliographic databases: MEDLINE, Embase, PsycINFO, ERIC, EconLit, SSRN, 3ie DEP · Boolean operators, truncation, wildcards, and MeSH term mapping · Constructing a search string from PICO to a multi-database strategy · Grey literature: government reports, thesis databases, conference proceedings · Citation searching: forward (citing) and backward (reference lists) · Reference management: Zotero and Mendeley — deduplication and organisation · Screening: Rayyan, Covidence, and Abstrackr — dual independent screening · Inter-rater reliability: Cohen’s kappa and acceptable agreement thresholds · PRISMA 2020 flow diagram: documenting every stage of search and selection · Hands-on lab: multi-database search + PRISMA 2020 flow diagram |
Designing a standardised data extraction form with all required fields · Piloting data extraction: testing on two studies before full extraction · Handling missing data: author contact, assumptions, and sensitivity analysis · Risk of Bias — RCTs: Cochrane RoB 2.0 — five domains and signalling questions · Risk of Bias — non-randomised studies: ROBINS-I tool — seven domains · Qualitative studies: CASP checklist and GRADE-CERQual framework · Mixed-methods: MMAT (Mixed Methods Appraisal Tool) · Publication bias: funnel plot asymmetry, Egger’s test, and trim-and-fill · GRADE framework: rating certainty of evidence — high to very low · Hands-on lab: extract data from 5 RCTs and complete RoB 2.0 for each |
When to pool: clinical and statistical heterogeneity assessment · Heterogeneity statistics: I², Cochran’s Q, and τ² (between-study variance) · Effect size measures: SMD (Cohen’s d, Hedges’ g), odds ratio, risk ratio · Fixed-effect model: inverse-variance weighting and assumptions · Random-effects model: DerSimonian-Laird and REML estimators · Forest plots: construction, weights, diamonds, and confidence intervals · Subgroup analysis: pre-specified vs post-hoc and interaction tests · Meta-regression: explaining heterogeneity with study-level covariates · Sensitivity analysis: one-study-removed and influence analysis · Network meta-analysis: introduction to multi-intervention comparison · Software: RevMan 5, R (meta and metafor packages), and Stata (metan) · Hands-on lab: complete meta-analysis in R — forest plot and I² interpretation |
PRISMA 2020 checklist: all 27 items for complete, transparent reporting · Writing the methods section: sufficient detail for independent replication · Writing results: narrative synthesis alongside statistical meta-analytic results · Writing discussion: limitations, applicability, and policy implications · Summary of Findings (SoF) tables: Cochrane format with GRADE ratings · Policy briefs from systematic reviews: 3ie, J-PAL, and WWC formats · Academic publication: Cochrane Library, Campbell Reviews, and peer-reviewed journals · Living systematic reviews: concept, maintenance schedule, and published examples · Capstone: submit a PROSPERO/OSF-registered protocol for a real policy question |
Design, conduct, and report a systematic review to Cochrane and Campbell standards · Execute comprehensive search strategies across bibliographic and grey literature · Conduct meta-analyses in R, produce forest plots, and interpret heterogeneity · Apply GRADE to rate certainty of evidence and communicate findings to policymakers · Achieve a credential benchmarked against Cochrane, 3ie, Campbell, and PROSPERO standards |
Complete all 6 modules, pass a 55-question proctored examination (minimum 75%), submit a protocol registered on PROSPERO or OSF, and complete a meta-analysis exercise producing a forest plot and GRADE Summary of Findings table. |
Evidence Synthesis Specialist, Systematic Reviewer, Research Analyst, Policy Analyst, Public Health Researcher, WHO/World Bank/3ie/J-PAL Research Staff. Average starting salary: $55,000–$90,000 USD. |
CSOP — Certified Survey Operations Professional (CSOP)
★ Why this certification was added
Grounded in the lead consultant’s real IITA-CGIAR field experience: coordinating household surveys of 1,536 farmers, training 69 enumerators across 4 states, achieving 91.1% panel retention at endline, and conducting attrition analysis confirming no systematic bias. Development organisations, NGOs, CGIAR centres, government ministries, and research institutions globally need trained survey professionals. No other platform teaches this with this level of real-world CGIAR-backed credibility.
“Real surveys. Real data. Real impact. Taught by someone who has done it at scale.”
A professional survey operations certification grounded in real IITA-CGIAR household survey experience. Covers survey design, ODK/KoboToolbox programming, multi-stage sampling, enumerator training and supervision, CAPI data collection, data quality control, panel retention strategies, attrition analysis, and survey reporting — taught by a practitioner who has coordinated surveys of over 1,500 households across multiple Nigerian states.
| Programme Details | Information |
|---|---|
|
Level |
University & Professional |
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Audience |
Development researchers, NGO monitoring and evaluation officers, government data officers, CGIAR and UN agency staff, agricultural researchers, public health professionals, and postgraduate students conducting primary data collection |
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Standards |
CGIAR Research Standards · World Bank LSMS Survey Standards · J-PAL Data Collection Standards · ODK/KoboToolbox Technical Standards · USAID Data Quality Assessment (DQA) Standards · FAO Household Survey Guidelines |
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Duration |
7 months (1 month per module) |
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Format |
Self-paced · Live instructor-led · Cohort-based · Blended |
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Assessment |
Proctored online examination (minimum 75%) + ODK/KoboToolbox questionnaire programming exercise + mock enumerator training plan |
|
Certificate |
CSOP Certificate — Ukeh-Adah Alliance Services Ltd |
Course modules
What is survey research and why it is the backbone of development evidence · Types of surveys: cross-sectional, longitudinal, panel, and cohort designs · Survey lifecycle: design, programming, training, fieldwork, quality control, analysis · Stakeholder management: working with research PIs, NGOs, governments, and donors · Ethics in survey research: informed consent, IRB approval, data confidentiality, and do-no-harm · Survey budgeting: estimating costs for enumerators, transport, data, and supervision · Real case study: IITA-CGIAR Tomato Baseline Survey — 1,536 households, Kano State, Nigeria |
Research question translation: from hypothesis to measurable survey questions · Question types: closed, open-ended, Likert scales, ranking, and filter questions · Survey sections: household characteristics, agricultural practices, income, food security, markets · Questionnaire flow: skip logic design, routing, and branching structures · Avoiding common errors: leading questions, double-barrelled, ambiguous, and loaded questions · Cognitive testing and piloting: testing your questionnaire before field deployment · CAPI vs PAPI: when to use tablet-based vs paper-based data collection · Focus Group Discussion (FGD) guides: design, facilitation notes, and recording protocols · Real example: reviewing the IITA Tomato and Cassava survey instruments |
Introduction to ODK (Open Data Kit): architecture, Central server, and Collect app · Setting up ODK Central: creating projects, forms, and user accounts · XLSForm design: survey, choices, and settings sheets — all question types · Advanced XLSForm: skip logic (relevant), constraints, required fields, calculations, and repeat groups · Media in ODK: embedding images, audio, and video into survey questions · KoboToolbox: interface, form builder, and comparison with ODK · GPS and geospatial data collection: capturing coordinates of households and fields · Real-time data monitoring: tracking submissions, checking completeness, and flagging issues · Hands-on lab: programme a complete 30-question household survey in ODK with skip logic and GPS capture |
Why sampling matters: census vs sample — practical and statistical reasons · Probability sampling: simple random, systematic, stratified, and cluster sampling · Multi-stage random sampling: the standard method for large household surveys · Sample size calculation: power, precision, intracluster correlation, and design effect · Listing and frame construction: building a sampling frame from a census or village list · Village and household selection: random number tables and lottery methods · Non-response and attrition: anticipated rates and their impact on sample size · Purposive sampling for qualitative work: key informants and focus group selection · Real case: multi-stage sampling design for 1,536 households across Kano State LGAs |
Enumerator profiles: what to look for in field data collectors · Recruitment process: advertising, screening, shortlisting, and practical tests · Training structure: classroom sessions, role-play, field pilot, and feedback · Training content: survey objectives, question-by-question walkthrough, ODK practice, and ethics · Field supervisor roles: monitoring data quality, resolving field issues, and daily reporting · Enumerator performance management: daily targets, data quality scores, and corrective action · Communication protocols: WhatsApp groups, daily check-ins, and escalation procedures · Common field problems and how to solve them: refusals, absent respondents, and data errors · Real case: co-facilitating IITA enumerator training — 26 participants (Kano) and 43 professionals (Benue) |
Data quality dimensions: completeness, accuracy, consistency, and timeliness · Back-checks: selecting households for re-interview and comparing responses · Logic and range checks: automated validation rules in ODK and STATA/R · Outlier detection: identifying implausible values and documenting data corrections · Data quality pause: when and how to stop data collection to fix systematic errors · Panel surveys: tracking respondents from baseline to endline across multiple rounds · Retention strategies: GPS mapping of households, local contact persons, and phone tracking · Attrition analysis: testing for systematic bias across treatment arms using STATA/R · Real case: 91.1% panel retention across 1,536 farmers — methods and lessons learned · Hands-on lab: conduct a back-check analysis and attrition test on a practice dataset |
Writing a survey methodology section: sampling, instruments, training, and data quality · Descriptive statistics for survey reports: weighted means, proportions, and confidence intervals · Presenting survey findings: tables, charts, and infographics for different audiences · Data documentation: codebooks, variable labels, and value labels for archiving · Survey report structure: executive summary, methodology, findings, limitations, and annexes · Sharing and archiving data: CGIAR GARDIAN, World Bank Microdata, and Harvard Dataverse · Policy briefs from survey data: writing for NGO, government, and donor audiences · Capstone project: design a complete survey — instrument, sampling frame, ODK programme, training plan, and mock methodology section |
Design complete household survey instruments with skip logic and CAPI programming in ODK · Apply multi-stage random sampling and calculate sample sizes with design effect corrections · Recruit, train, and supervise field enumerators to CGIAR and J-PAL standards · Implement data quality control systems including back-checks and logic checks · Conduct panel surveys and attrition analysis to detect and correct systematic bias · Achieve a credential grounded in real IITA-CGIAR large-scale survey operations experience |
Complete all 7 modules, pass a proctored examination (minimum 75%), submit a programmed ODK questionnaire with skip logic, and produce a mock enumerator training plan and field manual. |
Survey Coordinator, Field Research Manager, M&E Officer, Data Collection Specialist, Research Associate (CGIAR/NGO/UN), Agricultural Survey Analyst, Government Statistical Officer. Average starting salary: $40,000–$75,000 USD. |
CQR — Certified Qualitative Researcher (CQR)
★ Why this certification was added
Half of all academic and development research globally uses qualitative methods. The lead consultant has direct NVivo proficiency and real FGD facilitation experience from IITA-CGIAR cassava and tomato surveys. Currently the entire platform is quantitative-focused. Qualitative research methods are required in social sciences, public health, education, development studies, and humanities — and are demanded by Master’s and PhD students in the UK, US, and Nigeria who form the core client base.
“Understand the why behind the numbers. Qualitative research done right.”
A rigorous, internationally benchmarked qualitative research certification covering qualitative research design, interview and FGD facilitation, thematic and content analysis, NVivo for qualitative data management, mixed-methods integration, and academic write-up — grounded in real field research experience from IITA-CGIAR surveys.
| Programme Details | Information |
|---|---|
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Level |
University & Professional — Postgraduate level |
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Audience |
Master’s and PhD students, academic researchers, NGO programme staff, public health professionals, development economists, social scientists, and educators |
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Standards |
American Psychological Association (APA) Qualitative Research Standards · Consolidated Criteria for Reporting Qualitative Research (COREQ) · SRQR Reporting Standards · QSR NVivo Standards · CASP Qualitative Appraisal Checklist |
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Duration |
6 months (1 month per module) |
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Format |
Self-paced · Live instructor-led · Cohort-based · Blended |
|
Assessment |
Proctored online examination (minimum 75%) + NVivo analysis exercise + qualitative findings write-up |
|
Certificate |
CQR Certificate — Ukeh-Adah Alliance Services Ltd |
Course modules
What is qualitative research and when is it the right approach? · Philosophical foundations: ontology, epistemology, and paradigms (positivism, interpretivism, constructivism) · Qualitative research designs: phenomenology, grounded theory, ethnography, case study, and narrative inquiry · Comparing qualitative and quantitative: when to use each and when to mix · Rigour in qualitative research: credibility, transferability, dependability, and confirmability · Ethics in qualitative research: informed consent, confidentiality, anonymity, and power dynamics · Writing a qualitative research proposal: problem statement, questions, design, and rationale |
Interview types: structured, semi-structured, and unstructured interviews · Designing an interview guide: open questions, probes, and sensitive topic handling · Recruiting participants: purposive, snowball, and maximum variation sampling · Conducting in-depth interviews: building rapport, active listening, and managing silence · Focus Group Discussion (FGD) design: group composition, guide structure, and facilitation roles · FGD facilitation skills: managing dominant participants, drawing out quieter voices, and handling conflict · Recording and transcribing: audio recording, note-taking, and transcription conventions · Observation and field notes: participant observation and reflexive memo writing · Real case: FGD facilitation at IITA Cassava Survey training — 43 professionals, Benue State |
Preparing data for analysis: transcription, anonymisation, and organising files · Thematic analysis (Braun & Clarke 6-step framework): familiarisation, coding, theme development · Inductive vs deductive coding: bottom-up vs theory-driven approaches · Content analysis: manifest and latent content, coding frames, and frequency analysis · Discourse analysis: examining language, power, and social context · Narrative analysis: story structure, identity, and meaning-making · Framework analysis: structured approach for applied and policy research · Intercoder reliability: testing and improving consistency between coders · Writing analytical memos: documenting your interpretive thinking throughout analysis |
Introduction to NVivo: interface, projects, sources, and nodes · Importing data into NVivo: transcripts, PDFs, survey responses, audio, video, and social media · Creating and organising nodes (codes): free nodes, tree nodes, and case nodes · Coding in NVivo: highlighting and coding text, audio, and video data · Node queries: exploring coding patterns, word frequency, and text search · Matrix coding queries: cross-tabulating codes with case attributes (gender, location, etc.) · Visualisations in NVivo: word clouds, tree maps, cluster analysis, and mind maps · Case classification: organising participants by demographic attributes for comparison · Exporting NVivo outputs: coding summaries, matrices, and charts to Word and Excel · Hands-on lab: code a provided interview transcript in NVivo and produce a thematic summary report |
What is mixed methods research and when should you use it? · Mixed methods designs: explanatory sequential, exploratory sequential, convergent parallel, and embedded · Integration strategies: connecting, merging, and embedding qualitative and quantitative data · Using qualitative findings to explain quantitative results (and vice versa) · Joint displays: matrices and figures that integrate qualitative and quantitative findings · Rigour in mixed methods: validity threats and how to address them · Writing up mixed methods research: structure, integration, and transparency · Real example: integrating FGD findings with household survey data in agricultural research |
Structure of a qualitative findings chapter: themes, subthemes, and illustrative quotes · Using quotes effectively: selecting, introducing, and discussing participant voices · Reflexivity statement: acknowledging the researcher’s position and influence · Writing the qualitative methods section: design, sampling, data collection, and analysis · COREQ and SRQR checklists: ensuring complete and transparent reporting · Responding to qualitative peer review: common critiques and how to address them · Capstone project: conduct a mini qualitative analysis of a provided dataset in NVivo — produce a coded project, a thematic summary, and an 800-word findings write-up |
Design and conduct qualitative research studies across multiple traditions and paradigms · Facilitate in-depth interviews and Focus Group Discussions to professional standards · Apply thematic, content, and framework analysis to real qualitative datasets · Use NVivo to manage, code, query, and visualise qualitative data · Write up qualitative findings to academic publication and dissertation standards · Achieve a credential aligned to APA, COREQ, SRQR, and NVivo international standards |
Complete all 6 modules, pass a proctored examination (minimum 75%), submit a coded NVivo project file, and write up qualitative findings of at least 800 words from a provided dataset. |
Qualitative Researcher, Research Analyst, Programme Evaluator, Social Scientist, Public Health Researcher, Policy Analyst, Academic Lecturer. Average starting salary: $45,000–$80,000 USD. |
CGIS — Certified GIS & Remote Sensing Analyst (CGIS)
★ Why this certification was added
GIS and Remote Sensing are now fundamental tools in agricultural research, environmental monitoring, public health, urban planning, and development economics. CGIAR, FAO, World Bank, and every major development organisation uses spatial analysis. The lead consultant’s IITA-CGIAR surveys involve GPS data collection, household mapping, and spatial analysis of agricultural areas — making GIS a natural and credible extension. Expert instructors are available to deliver this to world-class standard.
“See the world differently. Every problem has a location.” A comprehensive GIS and Remote Sensing certification covering spatial data fundamentals, QGIS, ArcGIS Online, satellite imagery analysis, land use mapping, GPS field data collection, spatial statistics, and GIS applications in agriculture, public health, environmental monitoring, and development research. |
| Programme Details | Information |
|---|---|
|
Level |
University & Professional |
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Audience |
Agricultural researchers, environmental scientists, public health professionals, urban planners, development researchers, NGO programme officers, government land use officers, survey coordinators, and postgraduate students in geography, environmental science, and development studies |
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Standards |
ESRI ArcGIS Desktop Associate · QGIS Certification (OSGeo) · FAO Spatial Data Infrastructure Standards · CGIAR Spatial Data Standards · ISO 19115 Geographic Metadata · Copernicus Land Monitoring Service · USGS Earth Observation Standards |
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Duration |
7 months (1 month per module) |
|
Format |
Self-paced · Live instructor-led · Cohort-based · Blended |
|
Assessment |
Proctored online examination (minimum 75%) + GIS project with 3 professional maps, spatial analysis outputs, and a technical report |
|
Certificate |
CGIS Certificate — Ukeh-Adah Alliance Services Ltd |
Course modules
What is GIS? Definition, history, and real-world applications in agriculture, health, and environment · Spatial thinking: location, distance, pattern, and spatial relationships · Coordinate systems and map projections: geographic vs projected, datum concepts · Types of spatial data: vector (points, lines, polygons) and raster (grids, pixels) · Metadata and spatial data documentation: ISO 19115 standards · GIS applications: agriculture, health, environment, urban planning, disaster management · Introduction to QGIS and ArcGIS Online: installation, interface, plugins, project setup · Overview of free spatial data sources: OpenStreetMap, GADM, SRTM, WorldPop |
GPS field data collection: devices, accuracy, and data recording methods · ODK and KoboToolbox for spatial data: geopoint, geotrace, and geoshape question types · Importing GPS and ODK data into QGIS: CSV coordinates, GPX, and KML files · Downloading satellite imagery: Copernicus Open Access Hub, USGS Earth Explorer · Vector data operations: creating, editing, and attributing features · Raster data management: importing, clipping, reprojecting, and resampling · Spatial databases: PostGIS and SpatiaLite — storing and querying with SQL · Data quality in GIS: positional accuracy, topological errors, and validation · Hands-on lab: collect GPS field data with ODK and create a field survey map in QGIS |
Principles of map design: visual hierarchy, colour, generalisation, and balance · Thematic maps: choropleth, graduated symbol, dot density, and proportional symbol · Map elements: title, legend, north arrow, scale bar, source, and projection note · Colour in cartography: sequential, diverging, and qualitative colour schemes (ColorBrewer) · Layout design in QGIS: print layout, map frames, legends, and export settings · Multi-map layouts: inset maps, overview maps, and multi-panel comparisons · Map export: PDF, PNG, and SVG for reports, policy briefs, and presentations · Designing maps for different audiences: technical, policy, and public communication · Hands-on lab: produce three professional-quality thematic maps from a provided dataset |
Vector spatial analysis: buffer, clip, intersect, union, dissolve, and spatial join · Point pattern analysis: kernel density estimation and nearest neighbour · Network analysis: shortest path, service area, and routing in QGIS · Raster analysis: map algebra, slope, aspect, hillshade from digital elevation model · Terrain analysis: watershed delineation and flow accumulation · Interpolation: IDW, Kriging, and spline — continuous surfaces from point data · Zonal statistics: summarising raster values within polygon boundaries · Site suitability analysis: multi-criteria evaluation (MCE) for land use decisions · Hands-on lab: site suitability analysis for an agricultural or infrastructure project |
Electromagnetic spectrum and remote sensing principles: how satellites see the Earth · Optical sensors: Landsat 8/9, Sentinel-2, MODIS — spatial, spectral, temporal resolution · SAR: Sentinel-1 — all-weather, day/night imaging · Downloading and preprocessing imagery: atmospheric correction and cloud masking · Band combinations and indices: NDVI, NDWI, EVI, SAVI for vegetation and water analysis · Land Use and Land Cover (LULC) classification: supervised and unsupervised methods · Change detection: before-after analysis and multi-temporal image comparison · Crop mapping: agricultural field delineation and crop type identification · Flood and drought monitoring: satellite imagery for disaster response · Introduction to Google Earth Engine: JavaScript basics for large-scale Earth observation · Hands-on lab: calculate NDVI from Sentinel-2 and map vegetation change over time |
Agricultural GIS: crop mapping, yield estimation, pest spread, agro-climatic zoning · Precision agriculture: field monitoring, UAV/drone data, and variable rate applications · Food security mapping: IPC maps and spatial food security analysis · Public health GIS: disease mapping, health facility accessibility, outbreak analysis · Malaria and vector-borne disease: spatial risk models and intervention targeting · WASH GIS: facility mapping, sanitation coverage, and water access analysis · Disaster risk mapping: flood zones, landslide susceptibility, humanitarian response · CGIAR spatial research: GIS use in IITA, CIMMYT, IRRI, and ILRI programmes · Hands-on lab: map health facility accessibility or crop suitability for a development context |
ArcGIS Online: cloud-based GIS, web maps, story maps, and dashboards for sharing · Python for GIS: GeoPandas, Shapely, Fiona, and Rasterio for automated spatial workflows · Spatial statistics: Moran’s I (spatial autocorrelation) and hotspot analysis (Getis-Ord Gi*) · Joining GIS results with statistical analysis in STATA and R · Writing a GIS technical report: methodology, maps, discussion, and limitations · Capstone project: choose a topic (agricultural mapping, health access, LULC change, or flood risk) — collect or download data, conduct spatial analysis, produce 3+ professional maps, and write a technical report |
Collect, manage, and analyse spatial data using QGIS, ArcGIS Online, and Google Earth Engine · Produce professional-quality cartographic outputs for research, policy, and field operations · Analyse satellite imagery to map land cover, vegetation, agricultural areas, and water bodies · Process UAV/drone data using OpenDroneMap to produce orthomosaics, DSMs, and point clouds · Apply GIS to agricultural research, public health, disaster management, and development · Conduct spatial statistics including Moran’s I, kernel density, and multi-criteria suitability analysis · Achieve a credential aligned to ESRI ArcGIS Associate, OSGeo QGIS, CGIAR, and FAO spatial standards |
Complete all 7 modules, pass a proctored examination (minimum 75%), and submit a GIS project including at least 3 original maps, a spatial analysis with statistical outputs, and a 1,500-word technical report. |
GIS Analyst, Remote Sensing Specialist, Spatial Data Analyst, Land Use Planner, Environmental GIS Officer, Agricultural GIS Analyst, Disaster Risk Analyst, CGIAR/FAO/World Bank GIS Officer. Average starting salary: $45,000–$85,000 USD. |
RESEARCH & FIELD EXPERIMENTS — YOUR TOOLS, YOUR WAY: PERSONALISED TOOL TRAINING PROGRAMME
Already using a specific research tool? Tell us — we will train you in it.
Researchers, postgraduate students, NGO evaluators, and development professionals all have their preferred tools — often chosen by their university department, their funding agency, or their organisation. We do not ask you to change your tools. We come to your tools. Whether your supervisor requires ATLAS.ti, your funder specifies REDCap, your department uses PASS for power analysis, or your organisation has a proprietary survey platform — we will train you.
Our standard research tools — already covered in our certifications
Statistical Analysis: IBM SPSS · STATA · EViews · R (meta, metafor, survey, srvyr, grf packages) · G*Power Qualitative Analysis: NVivo · ATLAS.ti (on request) · Dedoose (on request) Survey & Data Collection: ODK · KoboToolbox · SurveyCTO · CAPI · REDCap (introduction) Systematic Reviews: RevMan 5 · Covidence · Rayyan · Zotero · Mendeley · PROSPERO Field Experiment Analysis: STATA (ITT, LATE, DiD, PSM) · R (randomizr, ri2, grf packages) GIS & Spatial: QGIS · ArcGIS Online · Google Earth Engine · DHIS2 Reporting: R Markdown · Word · Excel
Bring your research tool — we train you in it
Every research context is unique. The following tools have been requested by researchers and postgraduate students — and we are ready to train in all of them and more
SAS · SAS Enterprise Guide · Mplus (SEM and multilevel modelling) · HLM · Lisrel · AMOS (structural equation modelling) · SmartPLS · Minitab · JMP · Gretl · WinBUGS · JAGS · Stan (Bayesian) · MLwiN (multilevel modelling) · OpenBUGS |
ATLAS.ti · Dedoose · MaxQDA · Quirkos · Transana (video and audio analysis) · Qualtrics (design and analysis) · REDCap (electronic data capture for clinical and research) |
Survey Solutions (World Bank CAPI) · SurveyCTO advanced · Magpi · Fulcrum · ArcGIS Survey123 · CSPro (Census and Survey Processing) · EpiInfo · KOBO advanced |
EpiData · OpenEpi · PHOEBE · WHO STEPwise tools · DHIS2 Tracker advanced · OpenMRS advanced · LIMS (Laboratory Information Systems) |
EViews advanced · RATS · TSP · Stata MP · Bloomberg Terminal data extraction · World Bank Open Data API · IMF data tools · FRED API · Ox (time series) |
ArcGIS Pro desktop · ERDAS IMAGINE · ENVI (remote sensing) · SNAP (Sentinel Application Platform) · OpenDroneMap advanced · Pix4D |
Rayyan advanced · Covidence · DistillerSR · EPPI-Reviewer · JBI SUMARI · Cochrane RevMan advanced · GRADE Pro GDT |
How to request personalised research tool training
Contact us via WhatsApp, email, or the student portal — tell us your tool, your research context (PhD, NGO, government, publication), and your current level.
We respond within 24 hours with a personalised training plan — sessions, topics, and outcomes.
Training begins on your schedule — one-on-one, at your pace, on your actual data and research questions.
You receive a Certificate of Tool Training on completion naming the specific tool and skills covered. We have trained researchers from Nigeria, UK, US, Kenya, Ghana, and beyond. Your research tool is not a barrier — it is our starting point.
“Your research. Your tools. Your analysis. We are here to make it work.”
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